International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 05 | May -2017
p-ISSN: 2395-0072
www.irjet.net
Advanced Recommendation System Rohit Bhanushali1, Smeet Chitalia2, Prem Panchal3 123Student,
Dept. of Computer Engineering, K.J Somaiya Institute of Engineering and Information Technology, Sion, Mumbai-22, Maharashtra, India
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Abstract - Recommendation System has become more and
more popular in recent years. To facilitate the purchase process, many online stores provide a shopping recommendation system for their consumers. So far, the generic recommendation systems mainly consider preferences of a consumer based on his/her purchase history. But recommendation based only on consumer’s purchase history is a major drawback. If a consumer wishes to buy a product that he/she never bought then that recommendation system will fail. Our system tries to overcome this drawback. It not only checks purchase history but various other parameters come into play. Our system recommends products, music and movies based on location, browsing history of the users, ratings from different users and highly purchased products. System will recommend similar types of products for people having similar buying trends. It aims at supporting the users in various decision making processes such as what items to buy, what music to listen, which movie to watch, etc. In our new algorithm, we retrieve live ratings, data of news and music from Twitter. We believe that such a new scheme should be able to provide a better recommendation list which fit consumer’s desire.
A suggestion or proposal as to the best course of action, especially one put forward by an authoritative body.
1.2 Collaborative Filtering Collaborative Filtering (CF) is a technique used by some recommender systems. Collaborative filtering has two senses, a narrow one and a more general one. In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). Workflow of Collaborative Filtering: 1.
A user expresses his/her preferences by rating items of the system. These ratings can be viewed as an appropriate representation of the user’s interest in the corresponding domain.
2. The system matches this user's ratings against other users' and finds the people with most "similar" tastes.
Key Words: Recommendation, Collaborative Filtering, Clustering, Twitter, Classifier, Analysis, Product, Music, News.
3. With similar users, the system recommends items that the similar users have rated highly but not yet being rated by this user (presumably the absence of rating is often considered as the unfamiliarity of an item).
1. INTRODUCTION
1.3 Clustering
Most online stores provide a shopping recommendation system for the consumers to facilitate online shopping. The core of such systems is a personalized recommendation algorithm. This algorithm models consumer shopping behaviors and recommend items to the consumers while doing on-line purchasing. Since there is no explicit product rating available for shopping, the system has to estimate consumers’ preferences from their purchased histories. One of the major techniques used to develop a recommendation algorithm is collaborative filtering (CF). Nevertheless, the problem of inadequacy due to too few user ratings may make the formation of neighborhood inaccurate and there by results in poor recommendations.
Clustering or Cluster Analysis is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning. Clustering is a process of partitioning a set of data (or objects) into a set of meaningful sub-classes, called clusters. Help users understand the natural grouping or structure in a data set. Clustering: unsupervised classification: no predefined classes. Used either as a stand-alone tool to get insight into data distribution or as a pre-processing step for other algorithms. Moreover, data compression, outlier’s detection, understands human concept formation.
1.1 Recommendation
The act of saying that someone or something is good and deserves to be chosen.
A suggestion about what should be done.
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